Efficient Model Selectionn Through Standard Operating Procedure Using Hybrid Of Sparse And Robust Estimators

dc.contributor.author	Anam Javaid
dc.date.accessioned	2022-05-20T02:03:04Z
dc.date.available	2022-05-20T02:03:04Z
dc.date.issued	2020-11
dc.description.abstract	The Internet of Things (IoT) is becoming more critical as time passes by. The use of IoT-related products helps to reduce human effort and can provide the highest possible quality at a minimum of time. Solar dryer is one of the uses of IoT in the agricultural sector for the drying of goods. This study focuses on the identification of factors affecting the collector’s solar dryer efficiency and the removal of seaweed moisture ratio. The Standard Operational Procedure (SOP) is provided on the basis of four Phases for this purpose. A hybrid model based on a sparse and robust regression analysis is intended for this purpose. Six types of hybrid estimators are developed using sparse and robust estimators and the best combination is selected for the medium and large data set. Interaction effects in all possible models are primarily addressed in this study.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/15263
dc.publisher	Universiti Sains Malaysia	en_US
dc.subject	Efficient Model Selectionn Through Standard	en_US
dc.subject	Operating Procedure Using Hybrid Of Sparse And Robust Estimators	en_US
dc.title	Efficient Model Selectionn Through Standard Operating Procedure Using Hybrid Of Sparse And Robust Estimators	en_US
dc.type	Thesis	en_US

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